## Reading in : ./cel_files//A1H_(miRNA-4_0).CEL
## Reading in : ./cel_files//A2H_(miRNA-4_0).CEL
## Reading in : ./cel_files//A3H_(miRNA-4_0).CEL
## Reading in : ./cel_files//C1H_(miRNA-4_0).CEL
## Reading in : ./cel_files//C2H_(miRNA-4_0).CEL
## Reading in : ./cel_files//C3H_(miRNA-4_0).CEL
## Reading in : ./cel_files//F10_(miRNA-4_0).CEL
## Reading in : ./cel_files//F11_(miRNA-4_0).CEL
## Reading in : ./cel_files//K11_(miRNA-4_0).CEL
## Reading in : ./cel_files//K13_(miRNA-4_0).CEL
## Reading in : ./cel_files//L13_(miRNA-4_0).CEL
## Reading in : ./cel_files//L7_(miRNA-4_0).CEL
## Reading in : ./cel_files//P1_(miRNA-4_0).CEL
## Reading in : ./cel_files//P2_(miRNA-4_0).CEL
## Reading in : ./cel_files//P3_(miRNA-4_0).CEL
# Anotació fenotipica
pheno <- read.csv(file="./cel_files/Registremostres.csv",sep=";")
pheno<-pheno %>%
mutate(NaCl_envellides=ifelse(NaCl_envellides =="si"&PBS_NaCl=="si",1,
ifelse(NaCl_envellides =="si"&PBS_NaCl=="no",2,"no")))datatable_jm<-function(x,column=NULL){
# if(is.na(column)){column<-0}
datatable(
x,
extensions = 'Buttons',
filter = list(
position = 'top', clear = T
),
options = list(dom = 'Blfrtip',buttons = list(list(extend = 'colvis')),
buttons = list('copy', 'print',
list(extend = 'collection',
buttons = c('csv', 'excel', 'pdf'),
text = 'Download')),
columnDefs = list(list(visible=FALSE, targets=column))))}pheno$nom<-gsub("_[(]miRNA-4_0).CEL","",pheno$Nombre.Cel.file)
rownames(pheno)<-pheno$Nombre.Cel.file
# phenoData(data) <- AnnotatedDataFrame(pheno)
pheno_ano<-AnnotatedDataFrame(pheno)
data<-read.celfiles(celfiles, phenoData = pheno_ano)## Reading in : ./cel_files//A1H_(miRNA-4_0).CEL
## Reading in : ./cel_files//A2H_(miRNA-4_0).CEL
## Reading in : ./cel_files//A3H_(miRNA-4_0).CEL
## Reading in : ./cel_files//C1H_(miRNA-4_0).CEL
## Reading in : ./cel_files//C2H_(miRNA-4_0).CEL
## Reading in : ./cel_files//C3H_(miRNA-4_0).CEL
## Reading in : ./cel_files//F10_(miRNA-4_0).CEL
## Reading in : ./cel_files//F11_(miRNA-4_0).CEL
## Reading in : ./cel_files//K11_(miRNA-4_0).CEL
## Reading in : ./cel_files//K13_(miRNA-4_0).CEL
## Reading in : ./cel_files//L13_(miRNA-4_0).CEL
## Reading in : ./cel_files//L7_(miRNA-4_0).CEL
## Reading in : ./cel_files//P1_(miRNA-4_0).CEL
## Reading in : ./cel_files//P2_(miRNA-4_0).CEL
## Reading in : ./cel_files//P3_(miRNA-4_0).CEL
## Background correcting
## Normalizing
## Calculating Expression
library(dplyr)
df <- read.csv("/home/SHARED/annotation_affymetrix/miRNA-4_0-st-v1.annotations.20160922.csv",comment.char = "#")
df<-
df %>%
group_by(Accession) %>%
mutate(gene=paste(Accession, collapse="|"))
feat<-df[match(rownames(data_rma@featureData@data),df$Probe.Set.Name),]
feat<-AnnotatedDataFrame(feat)
rownames(feat@data)<-
rownames(data_rma@featureData)
data_rma@featureData<-feat
dup.ids <- feat@data$Accession[duplicated(feat@data$Accession)] %>%
unique %>%
sort
data_rma<-
data_rma[,pData(data_rma)$temps!=2|is.na(pData(data_rma)$temps)]
data_rma<-
data_rma[data_rma@featureData@data$Species.Scientific.Name=="Homo sapiens",]
data_rma<-data_rma[data_rma@featureData@data$Sequence.Type=="miRNA",]
exp_rma <- exprs(data_rma)# Filtre
comparativa<-"NaCl_envellides"
data_rma<-data_rma[,data_rma@phenoData@data[,comparativa]!="no"]
# mostres:
# mostres<-c("C2H","C1H","C3H","A1H","A2H","A3H")
# data_rma_f<-data_rma[,data_rma@phenoData@data$nom%in%mostres]
g1<-levels(as.factor(data_rma@phenoData@data[,comparativa]))[1]
g2<-levels(as.factor(data_rma@phenoData@data[,comparativa]))[2]
data_rma@phenoData@data[,comparativa]<-gsub(g1,"NaCl",data_rma@phenoData@data[,comparativa])
data_rma@phenoData@data[,comparativa]<-gsub(g2,"envellides",data_rma@phenoData@data[,comparativa])
g1<-"NaCl"
g2<-"envellides"
data_rma<-
data_rma[data_rma@featureData@data$Species.Scientific.Name=="Homo sapiens",]
data_rma<-data_rma[data_rma@featureData@data$Sequence.Type=="miRNA",]
data_rma_ind<-data_rma
exp_rma <- exprs(data_rma_ind)
phenotype_names <- ifelse(str_detect(pData(data_rma)[,comparativa],g1), g1, g2)
annotation_for_heatmap <- data.frame(Phenotype = phenotype_names)
row.names(annotation_for_heatmap) <- pData(data_rma_ind)$Id_de_la_muestra
dists <- as.matrix(dist(t(exp_rma), method = "manhattan"))
rownames(dists) <- pData(data_rma_ind)$Id_de_la_muestra
hmcol <- rev(colorRampPalette(RColorBrewer::brewer.pal(9, "YlOrRd"))(255))
colnames(dists) <- NULL
diag(dists) <- NA
ann_colors <- list(
Phenotype = c(g1 = "chartreuse4", g2 = "burlywood3")
)
names(ann_colors$Phenotype)<-c(g1,g2)
# png(paste0("resultats/","/PLOTS/heatmap.png"),width = 800,height = 800,res=150)
# pheatmap(dists, col = (hmcol),
#
# annotation_col = annotation_for_heatmap,
# annotation_colors = ann_colors,
# legend = TRUE,
# treeheight_row = 10,
# legend_breaks = c(min(dists, na.rm = TRUE),
# max(dists, na.rm = TRUE)),
# legend_labels = (c("small distance", "large distance")),
# main = "Heatmap calibrated samples")
.tmp<-dev.off()
# knitr::include_graphics("resultats/PLOTS/heatmap.png")groups = phenotype_names
f = factor(groups)
design = model.matrix(~ 0 + f)
colnames(design) = c(g1,g2)
data.fit = lmFit(exprs(data_rma_ind),design)
lev <- c(g1, g2)
# Parsing
a<-c(g1,"-",g2)
astr=paste(a, collapse="")
prestr="makeContrasts("
poststr=",levels=design)"
commandstr=paste(prestr,astr,poststr,sep="")
contrast.matrix=eval(parse(text=commandstr))
# colnames(contrast.matrix)<-paste(g1,"-",g2)
data.fit.con = contrasts.fit(data.fit,contrast.matrix)
data.fit.eb = eBayes(data.fit.con)
tab = topTable(data.fit.eb,coef=1,
# lfc = log2(1.5),
number=Inf,adjust.method="BH",genelist = data_rma_ind@featureData@data )
tab_all = topTable(data.fit.eb,coef=1,number=Inf,adjust.method="BH",genelist = data_rma_ind@featureData@data )
cols<-c("logFC","AveExpr")
cols1<-c("P.Value", "adj.P.Val")
FC_tab<-
tab %>% filter(P.Value<=0.05,abs(logFC)>=log2(1.5)) %>%
dplyr::select(-c(GeneChip.Array,Annotation.Date,Sequence,Sequence.Source,Probe.Set.ID,B,t,Probe.Set.Name,Alignments,Clustered.miRNAs.within.10kb,Genome.Context,Target.Genes)) %>%
mutate(across(cols, round, 3)) %>%
mutate(across(all_of(cols1), format.pval))
datatable_jm(FC_tab)mirna_int<-c("hsa-miR-4745-5p","hsa-miR-210-3p","hsa-miR-320a","hsa-miR-320b","hsa-miR-320c")
tab_all$diffexpressed <- "NO"
# if log2Foldchange > 0.6 and pvalue < 0.05, set as "UP"
tab_all$diffexpressed[tab_all$logFC > log2(1.5) & tab_all$P.Value < 0.05] <- "UP"
# if log2Foldchange < -0.6 and pvalue < 0.05, set as "DOWN"
tab_all$diffexpressed[tab_all$logFC < -log2(1.5) & tab_all$P.Value < 0.05] <- "DOWN"
tab_all$delabel <- NA
tab_all$delabel[tab_all$diffexpressed != "NO"] <- tab_all$Transcript.ID.Array.Design.[tab_all$diffexpressed != "NO"]
tab_all$delabel[!tab_all$delabel%in%mirna_int]<-NA
# plot adding up all layers we have seen so far
ggplot(data=tab_all, aes(x=logFC, y=-log10(P.Value), col=diffexpressed,
label=delabel))+
geom_point() +
theme_minimal() +
geom_text_repel() +
scale_color_manual(values=c("blue", "black", "red")) +
geom_vline(xintercept=c(-0.6, 0.6), col="red") +
geom_hline(yintercept=-log10(0.05), col="red")+
ggtitle("PL vs aP")gens_SIG<-tab %>% filter(P.Value<=0.05,abs(logFC)>=log2(1.5)) %>%
.$Probe.Set.Name
data_rma_SIG<-data_rma[data_rma@featureData@data$Probe.Set.Name%in%gens_SIG,]
data_rma_SIG_df<-exprs(data_rma_SIG)
rownames(data_rma_SIG_df)<-data_rma_SIG@featureData@data$Transcript.ID.Array.Design.
hmcol <- rev(colorRampPalette(RColorBrewer::brewer.pal(5, "YlOrRd"))(255))
colnames(dists) <- NULL
diag(dists) <- NA
ann_colors <- list(
Phenotype = c(g1 = "chartreuse4", g2 = "burlywood3")
)
names(ann_colors$Phenotype)<-c(g1,g2)
colnames(data_rma_SIG_df)<-gsub("_[(]miRNA-4_0).CEL","",colnames(data_rma_SIG_df))
p1<-pheatmap(show_rownames = F,
data_rma_SIG_df,cutree_rows = 2,cutree_cols = 2,
annotation_col = annotation_for_heatmap,
annotation_colors = ann_colors,scale = "row",
legend = F,
treeheight_row = 50,
legend_breaks = c(min(dists, na.rm = TRUE),
max(dists, na.rm = TRUE)),
legend_labels = (c("small distance", "large distance")),
main = "miRNA FC>1.5 & p<=0.05")
p1library(ComplexHeatmap)
mat<-data_rma_SIG_df
noms<-rownames(data_rma_SIG_df)
noms[!noms%in%mirna_int]<-""
top_annotation = HeatmapAnnotation(Group = anno_block(gp = gpar(fill = c("#BD7575", "#7EA669")),
labels = c("PL", "aP"),
labels_gp = gpar(col = "white", fontsize = 10)),
Samples = anno_boxplot(mat))
Heatmap(mat,name="Expression", row_labels = noms,
column_title = paste0("Top ",dim(mat)[1]," proteins DEG"),
rect_gp = gpar(col = "white", lwd = 0.5),
column_km = 2,
# row_split = factor(rep(c("A"),15)),
row_km = 2,
border = TRUE,
right_annotation = rowAnnotation(Proteins = anno_boxplot(mat)),
top_annotation = top_annotation
)library(multiMiR)
mirna_int<-c("hsa-miR-4745-5p","hsa-miR-210-3p","hsa-miR-320a","hsa-miR-320b","hsa-miR-320c")
targets <- get_multimir(mirna = mirna_int, summary = T)## Searching mirecords ...
## Searching mirtarbase ...
## Searching tarbase ...
##
## hsa-miR-210-3p hsa-miR-320a hsa-miR-320b hsa-miR-320c hsa-miR-4745-5p
## 3624 2045 1006 818 284
Gens disregulated in proteins and by miRNA
target_symbol<-
targets@data%>%
filter(mature_mirna_id ==mirna_int[1]) %>%
dplyr::select(target_symbol)
target_symbol<-(unique(target_symbol$target_symbol))
target_entrez<-
targets@data%>%
filter(mature_mirna_id ==mirna_int[1]) %>%
dplyr::select(target_entrez)
target_entrez<-(unique(target_entrez$target_entrez))
RNA_sig<-get(load("RNA_tab_sig"))
dim(RNA_sig)## [1] 369 10
GO_ORA_RNA_nu<-get(load("GO_ORA_nouniverse"))
for(i in 1:dim(GO_ORA_RNA_nu)[1]){
GO_ORA_RNA_nu$num_gens[i]<-sum(target_symbol%in%strsplit(GO_ORA_RNA_nu$geneID[i],"/")[[1]])
GO_ORA_RNA_nu$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(GO_ORA_RNA_nu$geneID[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(GO_ORA_RNA_nu %>% filter(num_gens>0))KEGG_ORA_RNA_nu<-get(load("kegg_sig_nouniverse"))
for(i in 1:dim(KEGG_ORA_RNA_nu)[1]){
KEGG_ORA_RNA_nu$num_gens[i]<-sum(target_symbol%in%strsplit(KEGG_ORA_RNA_nu$geneID[i],"/")[[1]])
KEGG_ORA_RNA_nu$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(KEGG_ORA_RNA_nu$geneID[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(KEGG_ORA_RNA_nu %>% filter(num_gens>0))GO_GSEA_RNA<-get(load("./GO_GSEA"))
for(i in 1:dim(GO_GSEA_RNA)[1]){
GO_GSEA_RNA$num_gens[i]<-sum(target_symbol%in%strsplit(GO_GSEA_RNA$core_enrichment[i],"/")[[1]])
GO_GSEA_RNA$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(GO_GSEA_RNA$core_enrichment[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(GO_GSEA_RNA%>% filter(num_gens>0))KEEG_GSEA_RNA<-get(load("./kegg_gsea_sig"))
for(i in 1:dim(KEEG_GSEA_RNA)[1]){
KEEG_GSEA_RNA$num_gens[i]<-sum(target_symbol%in%strsplit(KEEG_GSEA_RNA$core_enrichment[i],"/")[[1]])
KEEG_GSEA_RNA$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(KEEG_GSEA_RNA$core_enrichment[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(KEEG_GSEA_RNA%>% filter(num_gens>0))GO_ORA_mirna<-enrichGO(
target_symbol,
OrgDb=org.Hs.eg.db,
keyType = "SYMBOL",
ont = "BP",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
# universe=rownames(tab),
qvalueCutoff = 0.2,
# minGSSize = 1,
# maxGSSize = 5000,
readable = FALSE)
GO_ORA_mirna<-GO_ORA_mirna@result %>% filter(p.adjust<=0.05)
datatable_jm(GO_ORA_mirna,column = "geneID")# ORA
# miRNA comuns
# 2D Venn diagram
library(ggVennDiagram)
x <- list(GO_miRNA = GO_ORA_mirna$ID, GO_RNA_ORA = GO_ORA_RNA_nu$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))library(GOSim)
test<-getTermSim(c(GO_ORA_mirna$ID,GO_ORA_RNA_nu$ID))
test<-test[,GO_ORA_mirna$ID]
test<-test[!rownames(test)%in%GO_ORA_mirna$ID,]
test<-data.frame(test)
corr<-c()
for(i in 1:dim(test)[1]){
corr[i]<-paste0(colnames(test[i,])[test[i,]>0.7],collapse = ", ")
}
corr<-data.frame(corr)
rownames(corr)<-rownames(test)
corr<-corr %>% filter(corr!="")
GO_ORA_RNA_nu$GO_miRNA_1<-corr$corr[match(GO_ORA_RNA_nu$ID,rownames(corr))]
GO_ORA_RNA_nu<-
GO_ORA_RNA_nu %>% filter(!is.na(GO_miRNA_1))
datatable_jm(GO_ORA_RNA_nu,column = "geneID")# GSEA
# miRNA comuns
# 2D Venn diagram
library(ggVennDiagram)
x <- list(GO_miRNA = GO_ORA_mirna$ID, GO_RNA_GSEA = GO_GSEA_RNA$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))library(GOSim)
test<-getTermSim(c(GO_ORA_mirna$ID,GO_GSEA_RNA$ID))
test<-test[,GO_ORA_mirna$ID]
test<-test[!rownames(test)%in%GO_ORA_mirna$ID,]
test<-data.frame(test)
corr<-c()
for(i in 1:dim(test)[1]){
corr[i]<-paste0(colnames(test[i,])[test[i,]>0.7],collapse = ", ")
}
corr<-data.frame(corr)
rownames(corr)<-rownames(test)
corr<-corr %>% filter(corr!="")
GO_GSEA_RNA$GO_miRNA_1<-corr$corr[match(GO_GSEA_RNA$ID,rownames(corr))]
GO_GSEA_RNA<-GO_GSEA_RNA %>% filter(!is.na(GO_miRNA_1))
datatable_jm(GO_GSEA_RNA,column = "core_enrichment")kegg<-enrichKEGG(target_entrez,
organism = "hsa",
# universe = rownames(tab),
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
qvalueCutoff = 0.2,
use_internal_data = FALSE)
kegg<-kegg@result
kegg_sig<-kegg %>% filter(p.adjust<=0.05)
datatable_jm(kegg_sig)library(ggVennDiagram)
x <- list(KEGG_miRNA = kegg_sig$ID, KEGG_RNA_GSEA = kegg_gsea_sig$ID,
KEGG_RNA_ORA = KEGG_ORA_RNA_nu$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))Gens disregulated in proteins and by miRNA
target_symbol<-
targets@data%>%
filter(mature_mirna_id ==mirna_int[2]) %>%
dplyr::select(target_symbol)
target_symbol<-(unique(target_symbol$target_symbol))
target_entrez<-
targets@data%>%
filter(mature_mirna_id ==mirna_int[2]) %>%
dplyr::select(target_entrez)
target_entrez<-(unique(target_entrez$target_entrez))
RNA_sig<-get(load("RNA_tab_sig"))
dim(RNA_sig)## [1] 369 10
GO_ORA_RNA_nu<-get(load("GO_ORA_nouniverse"))
for(i in 1:dim(GO_ORA_RNA_nu)[1]){
GO_ORA_RNA_nu$num_gens[i]<-sum(target_symbol%in%strsplit(GO_ORA_RNA_nu$geneID[i],"/")[[1]])
GO_ORA_RNA_nu$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(GO_ORA_RNA_nu$geneID[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(GO_ORA_RNA_nu %>% filter(num_gens>0))KEGG_ORA_RNA_nu<-get(load("kegg_sig_nouniverse"))
for(i in 1:dim(KEGG_ORA_RNA_nu)[1]){
KEGG_ORA_RNA_nu$num_gens[i]<-sum(target_symbol%in%strsplit(KEGG_ORA_RNA_nu$geneID[i],"/")[[1]])
KEGG_ORA_RNA_nu$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(KEGG_ORA_RNA_nu$geneID[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(KEGG_ORA_RNA_nu %>% filter(num_gens>0))GO_GSEA_RNA<-get(load("./GO_GSEA"))
for(i in 1:dim(GO_GSEA_RNA)[1]){
GO_GSEA_RNA$num_gens[i]<-sum(target_symbol%in%strsplit(GO_GSEA_RNA$core_enrichment[i],"/")[[1]])
GO_GSEA_RNA$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(GO_GSEA_RNA$core_enrichment[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(GO_GSEA_RNA%>% filter(num_gens>0))KEEG_GSEA_RNA<-get(load("./kegg_gsea_sig"))
for(i in 1:dim(KEEG_GSEA_RNA)[1]){
KEEG_GSEA_RNA$num_gens[i]<-sum(target_symbol%in%strsplit(KEEG_GSEA_RNA$core_enrichment[i],"/")[[1]])
KEEG_GSEA_RNA$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(KEEG_GSEA_RNA$core_enrichment[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(KEEG_GSEA_RNA%>% filter(num_gens>0))GO_ORA_mirna<-enrichGO(
target_symbol,
OrgDb=org.Hs.eg.db,
keyType = "SYMBOL",
ont = "BP",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
# universe=rownames(tab),
qvalueCutoff = 0.2,
# minGSSize = 1,
# maxGSSize = 5000,
readable = FALSE)
GO_ORA_mirna<-GO_ORA_mirna@result %>% filter(p.adjust<=0.05)
datatable_jm(GO_ORA_mirna,column = "geneID")# ORA
# miRNA comuns
# 2D Venn diagram
library(ggVennDiagram)
x <- list(GO_miRNA = GO_ORA_mirna$ID, GO_RNA_ORA = GO_ORA_RNA_nu$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))test<-getTermSim(c(GO_ORA_mirna$ID,GO_ORA_RNA_nu$ID))
test<-test[,GO_ORA_mirna$ID]
test<-test[!rownames(test)%in%GO_ORA_mirna$ID,]
test<-data.frame(test)
corr<-c()
for(i in 1:dim(test)[1]){
corr[i]<-paste0(colnames(test[i,])[test[i,]>0.7],collapse = ", ")
}
corr<-data.frame(corr)
rownames(corr)<-rownames(test)
corr<-corr %>% filter(corr!="")
GO_ORA_RNA_nu$GO_miRNA_1<-corr$corr[match(GO_ORA_RNA_nu$ID,rownames(corr))]
GO_ORA_RNA_nu<-
GO_ORA_RNA_nu %>% filter(!is.na(GO_miRNA_1))
datatable_jm(GO_ORA_RNA_nu,column = "geneID")# GSEA
# miRNA comuns
# 2D Venn diagram
library(ggVennDiagram)
x <- list(GO_miRNA = GO_ORA_mirna$ID, GO_RNA_GSEA = GO_GSEA_RNA$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))library(GOSim)
test<-getTermSim(c(GO_ORA_mirna$ID,GO_GSEA_RNA$ID),verbose = T)
test<-test[,GO_ORA_mirna$ID]
test<-test[!rownames(test)%in%GO_ORA_mirna$ID,]
test<-data.frame(test)
corr<-c()
for(i in 1:dim(test)[1]){
corr[i]<-paste0(colnames(test[i,])[test[i,]>0.7],collapse = ", ")
}
corr<-data.frame(corr)
rownames(corr)<-rownames(test)
corr<-corr %>% filter(corr!="")
GO_GSEA_RNA$GO_miRNA_1<-corr$corr[match(GO_GSEA_RNA$ID,rownames(corr))]
GO_GSEA_RNA<-GO_GSEA_RNA %>% filter(!is.na(GO_miRNA_1))
datatable_jm(GO_GSEA_RNA,column = "core_enrichment")kegg<-enrichKEGG(target_entrez,
organism = "hsa",
# universe = rownames(tab),
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
qvalueCutoff = 0.2,
use_internal_data = FALSE)
kegg<-kegg@result
kegg_sig<-kegg %>% filter(p.adjust<=0.05)
datatable_jm(kegg_sig)library(ggVennDiagram)
x <- list(KEGG_miRNA = kegg_sig$ID, KEGG_RNA_GSEA = kegg_gsea_sig$ID,
KEGG_RNA_ORA = KEGG_ORA_RNA_nu$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))Gens disregulated in proteins and by miRNA
target_symbol<-
targets@data%>%
filter(mature_mirna_id ==mirna_int[3]) %>%
dplyr::select(target_symbol)
target_symbol<-(unique(target_symbol$target_symbol))
target_entrez<-
targets@data%>%
filter(mature_mirna_id ==mirna_int[3]) %>%
dplyr::select(target_entrez)
target_entrez<-(unique(target_entrez$target_entrez))
RNA_sig<-get(load("RNA_tab_sig"))
dim(RNA_sig)## [1] 369 10
GO_ORA_RNA_nu<-get(load("GO_ORA_nouniverse"))
for(i in 1:dim(GO_ORA_RNA_nu)[1]){
GO_ORA_RNA_nu$num_gens[i]<-sum(target_symbol%in%strsplit(GO_ORA_RNA_nu$geneID[i],"/")[[1]])
GO_ORA_RNA_nu$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(GO_ORA_RNA_nu$geneID[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(GO_ORA_RNA_nu %>% filter(num_gens>0))KEGG_ORA_RNA_nu<-get(load("kegg_sig_nouniverse"))
for(i in 1:dim(KEGG_ORA_RNA_nu)[1]){
KEGG_ORA_RNA_nu$num_gens[i]<-sum(target_symbol%in%strsplit(KEGG_ORA_RNA_nu$geneID[i],"/")[[1]])
KEGG_ORA_RNA_nu$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(KEGG_ORA_RNA_nu$geneID[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(KEGG_ORA_RNA_nu %>% filter(num_gens>0))GO_GSEA_RNA<-get(load("./GO_GSEA"))
for(i in 1:dim(GO_GSEA_RNA)[1]){
GO_GSEA_RNA$num_gens[i]<-sum(target_symbol%in%strsplit(GO_GSEA_RNA$core_enrichment[i],"/")[[1]])
GO_GSEA_RNA$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(GO_GSEA_RNA$core_enrichment[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(GO_GSEA_RNA%>% filter(num_gens>0))KEEG_GSEA_RNA<-get(load("./kegg_gsea_sig"))
for(i in 1:dim(KEEG_GSEA_RNA)[1]){
KEEG_GSEA_RNA$num_gens[i]<-sum(target_symbol%in%strsplit(KEEG_GSEA_RNA$core_enrichment[i],"/")[[1]])
KEEG_GSEA_RNA$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(KEEG_GSEA_RNA$core_enrichment[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(KEEG_GSEA_RNA%>% filter(num_gens>0))GO_ORA_mirna<-enrichGO(
target_symbol,
OrgDb=org.Hs.eg.db,
keyType = "SYMBOL",
ont = "BP",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
# universe=rownames(tab),
qvalueCutoff = 0.2,
# minGSSize = 1,
# maxGSSize = 5000,
readable = FALSE)
GO_ORA_mirna<-GO_ORA_mirna@result %>% filter(p.adjust<=0.05)
datatable_jm(GO_ORA_mirna,column = "geneID")# ORA
# miRNA comuns
# 2D Venn diagram
library(ggVennDiagram)
x <- list(GO_miRNA = GO_ORA_mirna$ID, GO_RNA_ORA = GO_ORA_RNA_nu$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))test<-getTermSim(c(GO_ORA_mirna$ID,GO_ORA_RNA_nu$ID))
test<-test[,GO_ORA_mirna$ID]
test<-test[!rownames(test)%in%GO_ORA_mirna$ID,]
test<-data.frame(test)
corr<-c()
for(i in 1:dim(test)[1]){
corr[i]<-paste0(colnames(test[i,])[test[i,]>0.7],collapse = ", ")
}
corr<-data.frame(corr)
rownames(corr)<-rownames(test)
corr<-corr %>% filter(corr!="")
GO_ORA_RNA_nu$GO_miRNA_1<-corr$corr[match(GO_ORA_RNA_nu$ID,rownames(corr))]
GO_ORA_RNA_nu<-
GO_ORA_RNA_nu %>% filter(!is.na(GO_miRNA_1))
datatable_jm(GO_ORA_RNA_nu,column = "geneID")# GSEA
# miRNA comuns
# 2D Venn diagram
library(ggVennDiagram)
x <- list(GO_miRNA = GO_ORA_mirna$ID, GO_RNA_GSEA = GO_GSEA_RNA$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))library(GOSim)
test<-getTermSim(c(GO_ORA_mirna$ID,GO_GSEA_RNA$ID),verbose = T)
test<-test[,GO_ORA_mirna$ID]
test<-test[!rownames(test)%in%GO_ORA_mirna$ID,]
test<-data.frame(test)
corr<-c()
for(i in 1:dim(test)[1]){
corr[i]<-paste0(colnames(test[i,])[test[i,]>0.7],collapse = ", ")
}
corr<-data.frame(corr)
rownames(corr)<-rownames(test)
corr<-corr %>% filter(corr!="")
GO_GSEA_RNA$GO_miRNA_1<-corr$corr[match(GO_GSEA_RNA$ID,rownames(corr))]
GO_GSEA_RNA<-GO_GSEA_RNA %>% filter(!is.na(GO_miRNA_1))
datatable_jm(GO_GSEA_RNA,column = "core_enrichment")kegg<-enrichKEGG(target_entrez,
organism = "hsa",
# universe = rownames(tab),
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
qvalueCutoff = 0.2,
use_internal_data = FALSE)
kegg<-kegg@result
kegg_sig<-kegg %>% filter(p.adjust<=0.05)
datatable_jm(kegg_sig)library(ggVennDiagram)
x <- list(KEGG_miRNA = kegg_sig$ID, KEGG_RNA_GSEA = kegg_gsea_sig$ID,
KEGG_RNA_ORA = KEGG_ORA_RNA_nu$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))Gens disregulated in proteins and by miRNA
target_symbol<-
targets@data%>%
filter(mature_mirna_id ==mirna_int[4]) %>%
dplyr::select(target_symbol)
target_symbol<-(unique(target_symbol$target_symbol))
target_entrez<-
targets@data%>%
filter(mature_mirna_id ==mirna_int[4]) %>%
dplyr::select(target_entrez)
target_entrez<-(unique(target_entrez$target_entrez))
RNA_sig<-get(load("RNA_tab_sig"))
dim(RNA_sig)## [1] 369 10
GO_ORA_RNA_nu<-get(load("GO_ORA_nouniverse"))
for(i in 1:dim(GO_ORA_RNA_nu)[1]){
GO_ORA_RNA_nu$num_gens[i]<-sum(target_symbol%in%strsplit(GO_ORA_RNA_nu$geneID[i],"/")[[1]])
GO_ORA_RNA_nu$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(GO_ORA_RNA_nu$geneID[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(GO_ORA_RNA_nu %>% filter(num_gens>0))KEGG_ORA_RNA_nu<-get(load("kegg_sig_nouniverse"))
for(i in 1:dim(KEGG_ORA_RNA_nu)[1]){
KEGG_ORA_RNA_nu$num_gens[i]<-sum(target_symbol%in%strsplit(KEGG_ORA_RNA_nu$geneID[i],"/")[[1]])
KEGG_ORA_RNA_nu$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(KEGG_ORA_RNA_nu$geneID[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(KEGG_ORA_RNA_nu %>% filter(num_gens>0))GO_GSEA_RNA<-get(load("./GO_GSEA"))
for(i in 1:dim(GO_GSEA_RNA)[1]){
GO_GSEA_RNA$num_gens[i]<-sum(target_symbol%in%strsplit(GO_GSEA_RNA$core_enrichment[i],"/")[[1]])
GO_GSEA_RNA$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(GO_GSEA_RNA$core_enrichment[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(GO_GSEA_RNA%>% filter(num_gens>0))KEEG_GSEA_RNA<-get(load("./kegg_gsea_sig"))
for(i in 1:dim(KEEG_GSEA_RNA)[1]){
KEEG_GSEA_RNA$num_gens[i]<-sum(target_symbol%in%strsplit(KEEG_GSEA_RNA$core_enrichment[i],"/")[[1]])
KEEG_GSEA_RNA$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(KEEG_GSEA_RNA$core_enrichment[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(KEEG_GSEA_RNA%>% filter(num_gens>0))GO_ORA_mirna<-enrichGO(
target_symbol,
OrgDb=org.Hs.eg.db,
keyType = "SYMBOL",
ont = "BP",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
# universe=rownames(tab),
qvalueCutoff = 0.2,
# minGSSize = 1,
# maxGSSize = 5000,
readable = FALSE)
GO_ORA_mirna<-GO_ORA_mirna@result %>% filter(p.adjust<=0.05)
datatable_jm(GO_ORA_mirna,column = "geneID")# ORA
# miRNA comuns
# 2D Venn diagram
library(ggVennDiagram)
x <- list(GO_miRNA = GO_ORA_mirna$ID, GO_RNA_ORA = GO_ORA_RNA_nu$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))test<-getTermSim(c(GO_ORA_mirna$ID,GO_ORA_RNA_nu$ID))
test<-test[,GO_ORA_mirna$ID]
test<-test[!rownames(test)%in%GO_ORA_mirna$ID,]
test<-data.frame(test)
corr<-c()
for(i in 1:dim(test)[1]){
corr[i]<-paste0(colnames(test[i,])[test[i,]>0.7],collapse = ", ")
}
corr<-data.frame(corr)
rownames(corr)<-rownames(test)
corr<-corr %>% filter(corr!="")
GO_ORA_RNA_nu$GO_miRNA_1<-corr$corr[match(GO_ORA_RNA_nu$ID,rownames(corr))]
GO_ORA_RNA_nu<-
GO_ORA_RNA_nu %>% filter(!is.na(GO_miRNA_1))
datatable_jm(GO_ORA_RNA_nu,column = "geneID")# GSEA
# miRNA comuns
# 2D Venn diagram
library(ggVennDiagram)
x <- list(GO_miRNA = GO_ORA_mirna$ID, GO_RNA_GSEA = GO_GSEA_RNA$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))library(GOSim)
test<-getTermSim(c(GO_ORA_mirna$ID,GO_GSEA_RNA$ID),verbose = T)
test<-test[,GO_ORA_mirna$ID]
test<-test[!rownames(test)%in%GO_ORA_mirna$ID,]
test<-data.frame(test)
corr<-c()
for(i in 1:dim(test)[1]){
corr[i]<-paste0(colnames(test[i,])[test[i,]>0.7],collapse = ", ")
}
corr<-data.frame(corr)
rownames(corr)<-rownames(test)
corr<-corr %>% filter(corr!="")
GO_GSEA_RNA$GO_miRNA_1<-corr$corr[match(GO_GSEA_RNA$ID,rownames(corr))]
GO_GSEA_RNA<-GO_GSEA_RNA %>% filter(!is.na(GO_miRNA_1))
datatable_jm(GO_GSEA_RNA,column = "core_enrichment")kegg<-enrichKEGG(target_entrez,
organism = "hsa",
# universe = rownames(tab),
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
qvalueCutoff = 0.2,
use_internal_data = FALSE)
kegg<-kegg@result
kegg_sig<-kegg %>% filter(p.adjust<=0.05)
datatable_jm(kegg_sig)library(ggVennDiagram)
x <- list(KEGG_miRNA = kegg_sig$ID, KEGG_RNA_GSEA = kegg_gsea_sig$ID,
KEGG_RNA_ORA = KEGG_ORA_RNA_nu$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))Gens disregulated in proteins and by miRNA
target_symbol<-
targets@data%>%
filter(mature_mirna_id ==mirna_int[5]) %>%
dplyr::select(target_symbol)
target_symbol<-(unique(target_symbol$target_symbol))
target_entrez<-
targets@data%>%
filter(mature_mirna_id ==mirna_int[5]) %>%
dplyr::select(target_entrez)
target_entrez<-(unique(target_entrez$target_entrez))
RNA_sig<-get(load("RNA_tab_sig"))
dim(RNA_sig)## [1] 369 10
GO_ORA_RNA_nu<-get(load("GO_ORA_nouniverse"))
for(i in 1:dim(GO_ORA_RNA_nu)[1]){
GO_ORA_RNA_nu$num_gens[i]<-sum(target_symbol%in%strsplit(GO_ORA_RNA_nu$geneID[i],"/")[[1]])
GO_ORA_RNA_nu$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(GO_ORA_RNA_nu$geneID[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(GO_ORA_RNA_nu %>% filter(num_gens>0))KEGG_ORA_RNA_nu<-get(load("kegg_sig_nouniverse"))
for(i in 1:dim(KEGG_ORA_RNA_nu)[1]){
KEGG_ORA_RNA_nu$num_gens[i]<-sum(target_symbol%in%strsplit(KEGG_ORA_RNA_nu$geneID[i],"/")[[1]])
KEGG_ORA_RNA_nu$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(KEGG_ORA_RNA_nu$geneID[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(KEGG_ORA_RNA_nu %>% filter(num_gens>0))GO_GSEA_RNA<-get(load("./GO_GSEA"))
for(i in 1:dim(GO_GSEA_RNA)[1]){
GO_GSEA_RNA$num_gens[i]<-sum(target_symbol%in%strsplit(GO_GSEA_RNA$core_enrichment[i],"/")[[1]])
GO_GSEA_RNA$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(GO_GSEA_RNA$core_enrichment[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(GO_GSEA_RNA%>% filter(num_gens>0))KEEG_GSEA_RNA<-get(load("./kegg_gsea_sig"))
for(i in 1:dim(KEEG_GSEA_RNA)[1]){
KEEG_GSEA_RNA$num_gens[i]<-sum(target_symbol%in%strsplit(KEEG_GSEA_RNA$core_enrichment[i],"/")[[1]])
KEEG_GSEA_RNA$gens[i]<-paste(target_symbol[target_symbol%in%strsplit(KEEG_GSEA_RNA$core_enrichment[i],"/")[[1]]],collapse = ", ")
}
datatable_jm(KEEG_GSEA_RNA%>% filter(num_gens>0))GO_ORA_mirna<-enrichGO(
target_symbol,
OrgDb=org.Hs.eg.db,
keyType = "SYMBOL",
ont = "BP",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
# universe=rownames(tab),
qvalueCutoff = 0.2,
# minGSSize = 1,
# maxGSSize = 5000,
readable = FALSE)
GO_ORA_mirna<-GO_ORA_mirna@result %>% filter(p.adjust<=0.05)
datatable_jm(GO_ORA_mirna,column = "geneID")# ORA
# miRNA comuns
# 2D Venn diagram
library(ggVennDiagram)
x <- list(GO_miRNA = GO_ORA_mirna$ID, GO_RNA_ORA = GO_ORA_RNA_nu$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))test<-getTermSim(c(GO_ORA_mirna$ID,GO_ORA_RNA_nu$ID))
test<-test[,GO_ORA_mirna$ID]
test<-test[!rownames(test)%in%GO_ORA_mirna$ID,]
test<-data.frame(test)
corr<-c()
for(i in 1:dim(test)[1]){
corr[i]<-paste0(colnames(test[i,])[test[i,]>0.7],collapse = ", ")
}
corr<-data.frame(corr)
rownames(corr)<-rownames(test)
corr<-corr %>% filter(corr!="")
GO_ORA_RNA_nu$GO_miRNA_1<-corr$corr[match(GO_ORA_RNA_nu$ID,rownames(corr))]
GO_ORA_RNA_nu<-
GO_ORA_RNA_nu %>% filter(!is.na(GO_miRNA_1))
datatable_jm(GO_ORA_RNA_nu,column = "geneID")# GSEA
# miRNA comuns
# 2D Venn diagram
library(ggVennDiagram)
x <- list(GO_miRNA = GO_ORA_mirna$ID, GO_RNA_GSEA = GO_GSEA_RNA$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))library(GOSim)
test<-getTermSim(c(GO_ORA_mirna$ID,GO_GSEA_RNA$ID),verbose = T)
test<-test[,GO_ORA_mirna$ID]
test<-test[!rownames(test)%in%GO_ORA_mirna$ID,]
test<-data.frame(test)
corr<-c()
for(i in 1:dim(test)[1]){
corr[i]<-paste0(colnames(test[i,])[test[i,]>0.7],collapse = ", ")
}
corr<-data.frame(corr)
rownames(corr)<-rownames(test)
corr<-corr %>% filter(corr!="")
GO_GSEA_RNA$GO_miRNA_1<-corr$corr[match(GO_GSEA_RNA$ID,rownames(corr))]
GO_GSEA_RNA<-GO_GSEA_RNA %>% filter(!is.na(GO_miRNA_1))
datatable_jm(GO_GSEA_RNA,column = "core_enrichment")kegg<-enrichKEGG(target_entrez,
organism = "hsa",
# universe = rownames(tab),
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
qvalueCutoff = 0.2,
use_internal_data = FALSE)
kegg<-kegg@result
kegg_sig<-kegg %>% filter(p.adjust<=0.05)
datatable_jm(kegg_sig)library(ggVennDiagram)
x <- list(KEGG_miRNA = kegg_sig$ID, KEGG_RNA_GSEA = kegg_gsea_sig$ID,
KEGG_RNA_ORA = KEGG_ORA_RNA_nu$ID)
ggVennDiagram(x,
show_intersect = F,
force_upset = F)+
# scale_fill_gradient2()+
scale_color_manual(values = c("black","black"))